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        "<a href=\"https://colab.research.google.com/github/034adarsh/Stock-Price-Prediction-Using-LSTM/blob/main/LSTM_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import all the required libraries\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "yrU0iWdalVGx"
      },
      "id": "yrU0iWdalVGx"
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      "id": "4189cf86",
      "metadata": {
        "scrolled": false,
        "id": "4189cf86"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import datetime as dt\n",
        "from datetime import date\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define start day to fetch the dataset from the yahoo finance library\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Nij98iZElfyQ"
      },
      "id": "Nij98iZElfyQ"
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    {
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      "execution_count": 2,
      "id": "d20a4c49",
      "metadata": {
        "scrolled": false,
        "id": "d20a4c49"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "START = \"2015-01-01\"\n",
        "TODAY = date.today().strftime(\"%Y-%m-%d\")\n",
        "\n",
        "# Define a function to load the dataset\n",
        "\n",
        "def load_data(ticker):\n",
        "    data = yf.download(ticker, START, TODAY)\n",
        "    data.reset_index(inplace=True)\n",
        "    return data"
      ]
    },
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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          "data": {
            "text/plain": [
              "        Date       Open       High        Low      Close  Adj Close     Volume\n",
              "0 2015-01-02  27.847500  27.860001  26.837500  27.332500  24.565701  212818400\n",
              "1 2015-01-05  27.072500  27.162500  26.352501  26.562500  23.873640  257142000\n",
              "2 2015-01-06  26.635000  26.857500  26.157499  26.565001  23.875881  263188400\n",
              "3 2015-01-07  26.799999  27.049999  26.674999  26.937500  24.210680  160423600\n",
              "4 2015-01-08  27.307501  28.037500  27.174999  27.972500  25.140909  237458000"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>27.072500</td>\n",
              "      <td>27.162500</td>\n",
              "      <td>26.352501</td>\n",
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              "      <td>23.873640</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>26.635000</td>\n",
              "      <td>26.857500</td>\n",
              "      <td>26.157499</td>\n",
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              "      <td>23.875881</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>26.799999</td>\n",
              "      <td>27.049999</td>\n",
              "      <td>26.674999</td>\n",
              "      <td>26.937500</td>\n",
              "      <td>24.210680</td>\n",
              "      <td>160423600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>27.307501</td>\n",
              "      <td>28.037500</td>\n",
              "      <td>27.174999</td>\n",
              "      <td>27.972500</td>\n",
              "      <td>25.140909</td>\n",
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              "                                                     [key], {});\n",
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      "source": [
        "data = load_data('AAPL')\n",
        "df=data\n",
        "df.head()"
      ]
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            "text/plain": [
              "        Open       High        Low      Close     Volume\n",
              "0  27.847500  27.860001  26.837500  27.332500  212818400\n",
              "1  27.072500  27.162500  26.352501  26.562500  257142000\n",
              "2  26.635000  26.857500  26.157499  26.565001  263188400\n",
              "3  26.799999  27.049999  26.674999  26.937500  160423600\n",
              "4  27.307501  28.037500  27.174999  27.972500  237458000"
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              "      <th>High</th>\n",
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              "      <th>Volume</th>\n",
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              "      <th>0</th>\n",
              "      <td>27.847500</td>\n",
              "      <td>27.860001</td>\n",
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              "      <td>27.332500</td>\n",
              "      <td>212818400</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27.072500</td>\n",
              "      <td>27.162500</td>\n",
              "      <td>26.352501</td>\n",
              "      <td>26.562500</td>\n",
              "      <td>257142000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.635000</td>\n",
              "      <td>26.857500</td>\n",
              "      <td>26.157499</td>\n",
              "      <td>26.565001</td>\n",
              "      <td>263188400</td>\n",
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              "      <th>3</th>\n",
              "      <td>26.799999</td>\n",
              "      <td>27.049999</td>\n",
              "      <td>26.674999</td>\n",
              "      <td>26.937500</td>\n",
              "      <td>160423600</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27.307501</td>\n",
              "      <td>28.037500</td>\n",
              "      <td>27.174999</td>\n",
              "      <td>27.972500</td>\n",
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              "                                                     [key], {});\n",
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      "source": [
        "df = df.drop(['Date', 'Adj Close'], axis = 1)\n",
        "df.head()"
      ]
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.title(\"Close Price Visualization\")\n",
        "plt.plot(df.Close)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "73888072",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "73888072",
        "outputId": "00dc415f-edf2-48f4-92b0-ee71f4042112"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open        High         Low       Close     Volume\n",
              "0      27.847500   27.860001   26.837500   27.332500  212818400\n",
              "1      27.072500   27.162500   26.352501   26.562500  257142000\n",
              "2      26.635000   26.857500   26.157499   26.565001  263188400\n",
              "3      26.799999   27.049999   26.674999   26.937500  160423600\n",
              "4      27.307501   28.037500   27.174999   27.972500  237458000\n",
              "...          ...         ...         ...         ...        ...\n",
              "2086  166.100006  167.410004  165.649994  166.470001   49923000\n",
              "2087  165.800003  168.160004  165.539993  167.630005   47720200\n",
              "2088  166.089996  167.869995  165.559998  166.649994   52456400\n",
              "2089  165.050003  166.449997  164.490005  165.020004   58311900\n",
              "2090  165.000000  165.600006  163.889999  165.330002   41922400\n",
              "\n",
              "[2091 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-57806e0b-cce0-4db7-aa13-0bac7fdb3119\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.847500</td>\n",
              "      <td>27.860001</td>\n",
              "      <td>26.837500</td>\n",
              "      <td>27.332500</td>\n",
              "      <td>212818400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27.072500</td>\n",
              "      <td>27.162500</td>\n",
              "      <td>26.352501</td>\n",
              "      <td>26.562500</td>\n",
              "      <td>257142000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.635000</td>\n",
              "      <td>26.857500</td>\n",
              "      <td>26.157499</td>\n",
              "      <td>26.565001</td>\n",
              "      <td>263188400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.799999</td>\n",
              "      <td>27.049999</td>\n",
              "      <td>26.674999</td>\n",
              "      <td>26.937500</td>\n",
              "      <td>160423600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27.307501</td>\n",
              "      <td>28.037500</td>\n",
              "      <td>27.174999</td>\n",
              "      <td>27.972500</td>\n",
              "      <td>237458000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2086</th>\n",
              "      <td>166.100006</td>\n",
              "      <td>167.410004</td>\n",
              "      <td>165.649994</td>\n",
              "      <td>166.470001</td>\n",
              "      <td>49923000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2087</th>\n",
              "      <td>165.800003</td>\n",
              "      <td>168.160004</td>\n",
              "      <td>165.539993</td>\n",
              "      <td>167.630005</td>\n",
              "      <td>47720200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2088</th>\n",
              "      <td>166.089996</td>\n",
              "      <td>167.869995</td>\n",
              "      <td>165.559998</td>\n",
              "      <td>166.649994</td>\n",
              "      <td>52456400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2089</th>\n",
              "      <td>165.050003</td>\n",
              "      <td>166.449997</td>\n",
              "      <td>164.490005</td>\n",
              "      <td>165.020004</td>\n",
              "      <td>58311900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2090</th>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.600006</td>\n",
              "      <td>163.889999</td>\n",
              "      <td>165.330002</td>\n",
              "      <td>41922400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2091 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57806e0b-cce0-4db7-aa13-0bac7fdb3119')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-57806e0b-cce0-4db7-aa13-0bac7fdb3119 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57806e0b-cce0-4db7-aa13-0bac7fdb3119');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting moving averages of 100 day\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "UMkTEOfBlzte"
      },
      "id": "UMkTEOfBlzte"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "114d2e0c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "114d2e0c",
        "outputId": "734fff73-936a-4bab-a8ad-351c8bd2ca8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            NaN\n",
              "1            NaN\n",
              "2            NaN\n",
              "3            NaN\n",
              "4            NaN\n",
              "          ...   \n",
              "2086    147.3198\n",
              "2087    147.4943\n",
              "2088    147.6501\n",
              "2089    147.8192\n",
              "2090    148.0303\n",
              "Name: Close, Length: 2091, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "ma100 = df.Close.rolling(100).mean()\n",
        "ma100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "e2f6b740",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "e2f6b740",
        "outputId": "b108baa8-775f-4709-f7d3-8c8eabbc0028"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Graph Of Moving Averages Of 100 Days')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "plt.plot(df.Close)\n",
        "plt.plot(ma100, 'r')\n",
        "plt.title('Graph Of Moving Averages Of 100 Days')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining 200 days moving averages and plotting comparision graph with 100 days moving averages\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "LQKvGsXwmN65"
      },
      "id": "LQKvGsXwmN65"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "46c89811",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46c89811",
        "outputId": "6520dee7-6658-4a24-8f8f-7144aed4df59"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0             NaN\n",
              "1             NaN\n",
              "2             NaN\n",
              "3             NaN\n",
              "4             NaN\n",
              "          ...    \n",
              "2086    150.01010\n",
              "2087    150.15360\n",
              "2088    150.27905\n",
              "2089    150.38955\n",
              "2090    150.48445\n",
              "Name: Close, Length: 2091, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "ma200 = df.Close.rolling(200).mean()\n",
        "ma200"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "d7c0ce2e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "d7c0ce2e",
        "outputId": "78eea70a-f8e6-4e59-a1de-838e5f283551"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Comparision Of 100 Days And 200 Days Moving Averages')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "plt.plot(df.Close)\n",
        "plt.plot(ma100, 'r')\n",
        "plt.plot(ma200, 'g')\n",
        "plt.title('Comparision Of 100 Days And 200 Days Moving Averages')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "45a099a7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45a099a7",
        "outputId": "6ee85271-92fd-4b96-9176-15e36e707b99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2090, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Spliting the dataset into training (70%) and testing (30%) set"
      ],
      "metadata": {
        "id": "bCw5fsrlmfpm"
      },
      "id": "bCw5fsrlmfpm"
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "9610c5fc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9610c5fc",
        "outputId": "23512f81-bb65-4fab-c31c-dca219041ec5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1463, 7)\n",
            "(627, 7)\n"
          ]
        }
      ],
      "source": [
        "# Splitting data into training and testing\n",
        "\n",
        "train = pd.DataFrame(data[0:int(len(data)*0.70)])\n",
        "test = pd.DataFrame(data[int(len(data)*0.70): int(len(data))])\n",
        "\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "2cb33dc0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2cb33dc0",
        "outputId": "403e717e-3fca-4880-8454-988eb9cfc1c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date       Open       High        Low      Close  Adj Close     Volume\n",
              "0 2015-01-02  27.847500  27.860001  26.837500  27.332500  24.565699  212818400\n",
              "1 2015-01-05  27.072500  27.162500  26.352501  26.562500  23.873646  257142000\n",
              "2 2015-01-06  26.635000  26.857500  26.157499  26.565001  23.875893  263188400\n",
              "3 2015-01-07  26.799999  27.049999  26.674999  26.937500  24.210686  160423600\n",
              "4 2015-01-08  27.307501  28.037500  27.174999  27.972500  25.140909  237458000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c9d29d2-877c-471f-962e-1610d0f4f58e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>27.847500</td>\n",
              "      <td>27.860001</td>\n",
              "      <td>26.837500</td>\n",
              "      <td>27.332500</td>\n",
              "      <td>24.565699</td>\n",
              "      <td>212818400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-01-05</td>\n",
              "      <td>27.072500</td>\n",
              "      <td>27.162500</td>\n",
              "      <td>26.352501</td>\n",
              "      <td>26.562500</td>\n",
              "      <td>23.873646</td>\n",
              "      <td>257142000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-06</td>\n",
              "      <td>26.635000</td>\n",
              "      <td>26.857500</td>\n",
              "      <td>26.157499</td>\n",
              "      <td>26.565001</td>\n",
              "      <td>23.875893</td>\n",
              "      <td>263188400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-07</td>\n",
              "      <td>26.799999</td>\n",
              "      <td>27.049999</td>\n",
              "      <td>26.674999</td>\n",
              "      <td>26.937500</td>\n",
              "      <td>24.210686</td>\n",
              "      <td>160423600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>27.307501</td>\n",
              "      <td>28.037500</td>\n",
              "      <td>27.174999</td>\n",
              "      <td>27.972500</td>\n",
              "      <td>25.140909</td>\n",
              "      <td>237458000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c9d29d2-877c-471f-962e-1610d0f4f58e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c9d29d2-877c-471f-962e-1610d0f4f58e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c9d29d2-877c-471f-962e-1610d0f4f58e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "91f5ef8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "91f5ef8b",
        "outputId": "a3241f79-660f-48b0-9ffa-e67d05710615"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "1463 2020-10-23  116.389999  116.550003  114.279999  115.040001  113.306618   \n",
              "1464 2020-10-26  114.010002  116.550003  112.879997  115.050003  113.316475   \n",
              "1465 2020-10-27  115.489998  117.279999  114.540001  116.599998  114.843124   \n",
              "1466 2020-10-28  115.050003  115.430000  111.099998  111.199997  109.524483   \n",
              "1467 2020-10-29  112.370003  116.930000  112.199997  115.320000  113.582390   \n",
              "\n",
              "         Volume  \n",
              "1463   82572600  \n",
              "1464  111850700  \n",
              "1465   92276800  \n",
              "1466  143937800  \n",
              "1467  146129200  "
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1463</th>\n",
              "      <td>2020-10-23</td>\n",
              "      <td>116.389999</td>\n",
              "      <td>116.550003</td>\n",
              "      <td>114.279999</td>\n",
              "      <td>115.040001</td>\n",
              "      <td>113.306618</td>\n",
              "      <td>82572600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1464</th>\n",
              "      <td>2020-10-26</td>\n",
              "      <td>114.010002</td>\n",
              "      <td>116.550003</td>\n",
              "      <td>112.879997</td>\n",
              "      <td>115.050003</td>\n",
              "      <td>113.316475</td>\n",
              "      <td>111850700</td>\n",
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              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>2020-10-27</td>\n",
              "      <td>115.489998</td>\n",
              "      <td>117.279999</td>\n",
              "      <td>114.540001</td>\n",
              "      <td>116.599998</td>\n",
              "      <td>114.843124</td>\n",
              "      <td>92276800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>115.050003</td>\n",
              "      <td>115.430000</td>\n",
              "      <td>111.099998</td>\n",
              "      <td>111.199997</td>\n",
              "      <td>109.524483</td>\n",
              "      <td>143937800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1467</th>\n",
              "      <td>2020-10-29</td>\n",
              "      <td>112.370003</td>\n",
              "      <td>116.930000</td>\n",
              "      <td>112.199997</td>\n",
              "      <td>115.320000</td>\n",
              "      <td>113.582390</td>\n",
              "      <td>146129200</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-00403570-901c-4a88-a482-0cb94c466f61 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-00403570-901c-4a88-a482-0cb94c466f61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using MinMax scaler for normalization of the dataset\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "-a818gdwm_WV"
      },
      "id": "-a818gdwm_WV"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "01f04df7",
      "metadata": {
        "id": "01f04df7"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_close = train.iloc[:, 4:5].values\n",
        "test_close = test.iloc[:, 4:5].values"
      ],
      "metadata": {
        "id": "53oSB51dB6Tw"
      },
      "id": "53oSB51dB6Tw",
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "d97e7ff8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d97e7ff8",
        "outputId": "8bf8560a-d7ec-4797-a55a-a8c7bb7720d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04254224],\n",
              "       [0.03564229],\n",
              "       [0.0356647 ],\n",
              "       ...,\n",
              "       [0.85062062],\n",
              "       [0.8448856 ],\n",
              "       [0.83484929]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "data_training_array = scaler.fit_transform(train_close)\n",
        "data_training_array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "10227978",
      "metadata": {
        "id": "10227978"
      },
      "outputs": [],
      "source": [
        "x_train = []\n",
        "y_train = [] \n",
        "\n",
        "for i in range(100, data_training_array.shape[0]):\n",
        "    x_train.append(data_training_array[i-100: i])\n",
        "    y_train.append(data_training_array[i, 0])\n",
        "\n",
        "x_train, y_train = np.array(x_train), np.array(y_train) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "0ea6446e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ea6446e",
        "outputId": "d7be262a-4cd6-4bbf-9f50-a13e111cc2ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1363, 100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML Model (LSTM)\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "dsMIlQ8DnVOJ"
      },
      "id": "dsMIlQ8DnVOJ"
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "e60ed317",
      "metadata": {
        "id": "e60ed317"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "from tensorflow.keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "17082610",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17082610",
        "outputId": "1bdf1b3b-dc08-4afe-9b51-d12fb3f1a874"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_8 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_9 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_10 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_11 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units = 50, activation = 'relu', return_sequences=True\n",
        "              ,input_shape = (x_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "model.add(LSTM(units = 60, activation = 'relu', return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "model.add(LSTM(units = 80, activation = 'relu', return_sequences=True))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "\n",
        "model.add(LSTM(units = 120, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(units = 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "5f5a1db3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5f5a1db3",
        "outputId": "fa46e211-69af-428f-beb2-73283a533bab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_8 (LSTM)               (None, 100, 50)           10400     \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 100, 50)           0         \n",
            "                                                                 \n",
            " lstm_9 (LSTM)               (None, 100, 60)           26640     \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 100, 60)           0         \n",
            "                                                                 \n",
            " lstm_10 (LSTM)              (None, 100, 80)           45120     \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 100, 80)           0         \n",
            "                                                                 \n",
            " lstm_11 (LSTM)              (None, 120)               96480     \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 120)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 121       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,761\n",
            "Trainable params: 178,761\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "KgEnUfCnn_1r"
      },
      "id": "KgEnUfCnn_1r"
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "1d0c9b64",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d0c9b64",
        "outputId": "6461b746-dd3a-443f-b303-d085ba34acc2"
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          "text": [
            "Epoch 1/50\n",
            "43/43 [==============================] - 41s 806ms/step - loss: 0.0021 - MAE: 0.0296 - val_loss: 4476.0820 - val_MAE: 64.9403\n",
            "Epoch 2/50\n",
            "43/43 [==============================] - 32s 748ms/step - loss: 0.0018 - MAE: 0.0282 - val_loss: 4482.5337 - val_MAE: 64.9902\n",
            "Epoch 3/50\n",
            "43/43 [==============================] - 34s 785ms/step - loss: 0.0014 - MAE: 0.0252 - val_loss: 4479.4561 - val_MAE: 64.9685\n",
            "Epoch 4/50\n",
            "43/43 [==============================] - 34s 783ms/step - loss: 0.0015 - MAE: 0.0258 - val_loss: 4478.7212 - val_MAE: 64.9634\n",
            "Epoch 5/50\n",
            "43/43 [==============================] - 35s 814ms/step - loss: 0.0016 - MAE: 0.0269 - val_loss: 4477.4165 - val_MAE: 64.9535\n",
            "Epoch 6/50\n",
            "43/43 [==============================] - 32s 742ms/step - loss: 0.0017 - MAE: 0.0274 - val_loss: 4482.0625 - val_MAE: 64.9908\n",
            "Epoch 7/50\n",
            "43/43 [==============================] - 34s 790ms/step - loss: 0.0022 - MAE: 0.0316 - val_loss: 4483.4443 - val_MAE: 64.9972\n",
            "Epoch 8/50\n",
            "43/43 [==============================] - 33s 776ms/step - loss: 0.0017 - MAE: 0.0267 - val_loss: 4482.7056 - val_MAE: 64.9907\n",
            "Epoch 9/50\n",
            "43/43 [==============================] - 32s 747ms/step - loss: 0.0018 - MAE: 0.0276 - val_loss: 4491.7139 - val_MAE: 65.0562\n",
            "Epoch 10/50\n",
            "43/43 [==============================] - 33s 782ms/step - loss: 0.0018 - MAE: 0.0287 - val_loss: 4483.1665 - val_MAE: 64.9964\n",
            "Epoch 11/50\n",
            "43/43 [==============================] - 34s 791ms/step - loss: 0.0014 - MAE: 0.0264 - val_loss: 4488.5894 - val_MAE: 65.0367\n",
            "Epoch 12/50\n",
            "43/43 [==============================] - 32s 744ms/step - loss: 0.0016 - MAE: 0.0276 - val_loss: 4479.2734 - val_MAE: 64.9669\n",
            "Epoch 13/50\n",
            "43/43 [==============================] - 33s 780ms/step - loss: 0.0017 - MAE: 0.0273 - val_loss: 4486.5806 - val_MAE: 65.0201\n",
            "Epoch 14/50\n",
            "43/43 [==============================] - 33s 773ms/step - loss: 0.0017 - MAE: 0.0274 - val_loss: 4475.9600 - val_MAE: 64.9440\n",
            "Epoch 15/50\n",
            "43/43 [==============================] - 32s 742ms/step - loss: 0.0018 - MAE: 0.0277 - val_loss: 4481.2207 - val_MAE: 64.9810\n",
            "Epoch 16/50\n",
            "43/43 [==============================] - 33s 780ms/step - loss: 0.0015 - MAE: 0.0263 - val_loss: 4477.8730 - val_MAE: 64.9594\n",
            "Epoch 17/50\n",
            "43/43 [==============================] - 32s 754ms/step - loss: 0.0016 - MAE: 0.0263 - val_loss: 4480.6182 - val_MAE: 64.9805\n",
            "Epoch 18/50\n",
            "43/43 [==============================] - 33s 757ms/step - loss: 0.0017 - MAE: 0.0271 - val_loss: 4486.5029 - val_MAE: 65.0191\n",
            "Epoch 19/50\n",
            "43/43 [==============================] - 33s 777ms/step - loss: 0.0020 - MAE: 0.0290 - val_loss: 4483.4663 - val_MAE: 64.9992\n",
            "Epoch 20/50\n",
            "43/43 [==============================] - 32s 750ms/step - loss: 0.0015 - MAE: 0.0261 - val_loss: 4480.8540 - val_MAE: 64.9808\n",
            "Epoch 21/50\n",
            "43/43 [==============================] - 33s 779ms/step - loss: 0.0015 - MAE: 0.0265 - val_loss: 4477.7344 - val_MAE: 64.9553\n",
            "Epoch 22/50\n",
            "43/43 [==============================] - 34s 794ms/step - loss: 0.0016 - MAE: 0.0266 - val_loss: 4488.8789 - val_MAE: 65.0390\n",
            "Epoch 23/50\n",
            "43/43 [==============================] - 32s 742ms/step - loss: 0.0015 - MAE: 0.0270 - val_loss: 4483.7148 - val_MAE: 65.0008\n",
            "Epoch 24/50\n",
            "43/43 [==============================] - 35s 812ms/step - loss: 0.0014 - MAE: 0.0256 - val_loss: 4482.4932 - val_MAE: 64.9899\n",
            "Epoch 25/50\n",
            "43/43 [==============================] - 33s 765ms/step - loss: 0.0015 - MAE: 0.0264 - val_loss: 4485.0425 - val_MAE: 65.0093\n",
            "Epoch 26/50\n",
            "43/43 [==============================] - 32s 752ms/step - loss: 0.0016 - MAE: 0.0271 - val_loss: 4481.9380 - val_MAE: 64.9861\n",
            "Epoch 27/50\n",
            "43/43 [==============================] - 34s 783ms/step - loss: 0.0018 - MAE: 0.0279 - val_loss: 4480.2969 - val_MAE: 64.9751\n",
            "Epoch 28/50\n",
            "43/43 [==============================] - 34s 800ms/step - loss: 0.0015 - MAE: 0.0261 - val_loss: 4481.7783 - val_MAE: 64.9885\n",
            "Epoch 29/50\n",
            "43/43 [==============================] - 31s 729ms/step - loss: 0.0016 - MAE: 0.0263 - val_loss: 4483.6357 - val_MAE: 64.9999\n",
            "Epoch 30/50\n",
            "43/43 [==============================] - 35s 807ms/step - loss: 0.0017 - MAE: 0.0263 - val_loss: 4489.8838 - val_MAE: 65.0442\n",
            "Epoch 31/50\n",
            "43/43 [==============================] - 34s 783ms/step - loss: 0.0018 - MAE: 0.0287 - val_loss: 4478.1865 - val_MAE: 64.9600\n",
            "Epoch 32/50\n",
            "43/43 [==============================] - 32s 744ms/step - loss: 0.0013 - MAE: 0.0242 - val_loss: 4479.4697 - val_MAE: 64.9709\n",
            "Epoch 33/50\n",
            "43/43 [==============================] - 33s 773ms/step - loss: 0.0015 - MAE: 0.0260 - val_loss: 4482.6650 - val_MAE: 64.9906\n",
            "Epoch 34/50\n",
            "43/43 [==============================] - 34s 792ms/step - loss: 0.0014 - MAE: 0.0249 - val_loss: 4478.0142 - val_MAE: 64.9568\n",
            "Epoch 35/50\n",
            "43/43 [==============================] - 32s 742ms/step - loss: 0.0014 - MAE: 0.0254 - val_loss: 4481.1772 - val_MAE: 64.9816\n",
            "Epoch 36/50\n",
            "43/43 [==============================] - 35s 814ms/step - loss: 0.0013 - MAE: 0.0247 - val_loss: 4478.9106 - val_MAE: 64.9646\n",
            "Epoch 37/50\n",
            "43/43 [==============================] - 33s 765ms/step - loss: 0.0016 - MAE: 0.0260 - val_loss: 4479.1162 - val_MAE: 64.9687\n",
            "Epoch 38/50\n",
            "43/43 [==============================] - 32s 748ms/step - loss: 0.0014 - MAE: 0.0251 - val_loss: 4479.2173 - val_MAE: 64.9673\n",
            "Epoch 39/50\n",
            "43/43 [==============================] - 33s 778ms/step - loss: 0.0014 - MAE: 0.0238 - val_loss: 4476.1841 - val_MAE: 64.9427\n",
            "Epoch 40/50\n",
            "43/43 [==============================] - 33s 779ms/step - loss: 0.0014 - MAE: 0.0253 - val_loss: 4476.8682 - val_MAE: 64.9506\n",
            "Epoch 41/50\n",
            "43/43 [==============================] - 32s 744ms/step - loss: 0.0012 - MAE: 0.0238 - val_loss: 4483.1069 - val_MAE: 64.9943\n",
            "Epoch 42/50\n",
            "43/43 [==============================] - 33s 781ms/step - loss: 0.0013 - MAE: 0.0241 - val_loss: 4480.4990 - val_MAE: 64.9774\n",
            "Epoch 43/50\n",
            "43/43 [==============================] - 34s 783ms/step - loss: 0.0016 - MAE: 0.0252 - val_loss: 4486.0117 - val_MAE: 65.0164\n",
            "Epoch 44/50\n",
            "43/43 [==============================] - 32s 740ms/step - loss: 0.0014 - MAE: 0.0244 - val_loss: 4481.1470 - val_MAE: 64.9831\n",
            "Epoch 45/50\n",
            "43/43 [==============================] - 34s 793ms/step - loss: 0.0014 - MAE: 0.0260 - val_loss: 4483.4121 - val_MAE: 64.9975\n",
            "Epoch 46/50\n",
            "43/43 [==============================] - 32s 752ms/step - loss: 0.0013 - MAE: 0.0240 - val_loss: 4482.8413 - val_MAE: 64.9950\n",
            "Epoch 47/50\n",
            "43/43 [==============================] - 32s 736ms/step - loss: 0.0013 - MAE: 0.0247 - val_loss: 4481.3086 - val_MAE: 64.9829\n",
            "Epoch 48/50\n",
            "43/43 [==============================] - 33s 780ms/step - loss: 0.0012 - MAE: 0.0238 - val_loss: 4481.4185 - val_MAE: 64.9836\n",
            "Epoch 49/50\n",
            "43/43 [==============================] - 33s 769ms/step - loss: 0.0014 - MAE: 0.0247 - val_loss: 4482.6431 - val_MAE: 64.9928\n",
            "Epoch 50/50\n",
            "43/43 [==============================] - 32s 734ms/step - loss: 0.0014 - MAE: 0.0246 - val_loss: 4480.3389 - val_MAE: 64.9758\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f239029e700>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['MAE'])\n",
        "model.fit(x_train, y_train, validation_data = (x_test, y_test) ,epochs = 50)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('keras_model.h5')"
      ],
      "metadata": {
        "id": "zG1W6DhzI-hP"
      },
      "id": "zG1W6DhzI-hP",
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "test_close.shape\n",
        "test_close"
      ],
      "metadata": {
        "id": "khcKI1sSJSJB"
      },
      "id": "khcKI1sSJSJB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "past_100_days = pd.DataFrame(train_close[-100:])"
      ],
      "metadata": {
        "id": "6QyF-7KbKaGt"
      },
      "id": "6QyF-7KbKaGt",
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = pd.DataFrame(test_close)"
      ],
      "metadata": {
        "id": "pKsjLCyGLuOz"
      },
      "id": "pKsjLCyGLuOz",
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Defining the final dataset for testing by including last 100 coloums of the training dataset to get the prediction from the 1st column of the testing dataset.**\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "id": "d9ZKAzB8oKXJ"
      },
      "id": "d9ZKAzB8oKXJ"
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = past_100_days.append(test_df, ignore_index = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUapqTcVLOj-",
        "outputId": "574ac38d-9500-4d04-a59c-63b999af43d5"
      },
      "id": "UUapqTcVLOj-",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-50640d85f7a0>:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  final_df = past_100_days.append(test_df, ignore_index = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.head()"
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      "execution_count": 58,
      "outputs": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = scaler.fit_transform(final_df)\n",
        "input_data"
      ],
      "metadata": {
        "id": "BU7BPmzvMZpZ"
      },
      "id": "BU7BPmzvMZpZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2E6k84TtMpvD",
        "outputId": "01e58a86-5d69-4a6c-9c0b-24a295bf6489"
      },
      "id": "2E6k84TtMpvD",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(727, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing the model\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "hJzkNc_To9li"
      },
      "id": "hJzkNc_To9li"
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = []\n",
        "y_test = []\n",
        "for i in range(100, input_data.shape[0]):\n",
        "   x_test.append(input_data[i-100: i])\n",
        "   y_test.append(input_data[i, 0])"
      ],
      "metadata": {
        "id": "d9xCof1CMwwh"
      },
      "id": "d9xCof1CMwwh",
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test, y_test = np.array(x_test), np.array(y_test)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MGDYZvrNUYc",
        "outputId": "edc06c42-97d9-49ab-db6f-4054bcf10f9a"
      },
      "id": "6MGDYZvrNUYc",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(627, 100, 1)\n",
            "(627,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making prediction and plotting the graph of predicted vs actual values\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "PjvHSsrYpFBG"
      },
      "id": "PjvHSsrYpFBG"
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions\n",
        "\n",
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLMExu7NNdmA",
        "outputId": "8038aa60-ecfb-4222-e8ad-edfc8f6c24a1"
      },
      "id": "GLMExu7NNdmA",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20/20 [==============================] - 2s 64ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAmVS__yNu-A",
        "outputId": "0bccada4-46f7-4a77-8eb9-6b46fcab85f8"
      },
      "id": "wAmVS__yNu-A",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFhTH-1aNyqt",
        "outputId": "28aac7e4-150f-4898-e81c-abf243c9b01c"
      },
      "id": "HFhTH-1aNyqt",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       0.27881298, 0.27792563, 0.29439025, 0.33885436, 0.37907917,\n",
              "       0.37572714, 0.35236124, 0.3489106 , 0.3836143 , 0.38085379,\n",
              "       0.38134677, 0.39160016, 0.38262842, 0.36922015, 0.37523415,\n",
              "       0.3624174 , 0.32800946, 0.34102335, 0.34950211, 0.35502314,\n",
              "       0.37927639, 0.41545896, 0.41900821, 0.41762796, 0.41082521,\n",
              "       0.42561374, 0.43182489, 0.40619146, 0.42058562, 0.41240269,\n",
              "       0.40619146, 0.46633145, 0.46564132, 0.47441584, 0.45430351,\n",
              "       0.46978209, 0.50576759, 0.49669731, 0.50665487, 0.55318944,\n",
              "       0.53524595, 0.52390815, 0.51375337, 0.48141581, 0.49719014,\n",
              "       0.45371192, 0.49630287, 0.50744361, 0.47717635, 0.4754018 ,\n",
              "       0.49600711, 0.4764863 , 0.4590358 , 0.46583854, 0.50724638,\n",
              "       0.55496399, 0.57665395, 0.61461107, 0.61697729, 0.60613231,\n",
              "       0.55713298, 0.50655633, 0.52804891, 0.53642914, 0.52607714,\n",
              "       0.56009072, 0.55387949, 0.55535843, 0.54648523, 0.54037269,\n",
              "       0.5378094 , 0.54017546, 0.51868288, 0.49551413, 0.48437354,\n",
              "       0.48595087, 0.44779652, 0.44641627, 0.44138815, 0.39840283,\n",
              "       0.40106481, 0.46544417, 0.43912062, 0.40895197, 0.38992407,\n",
              "       0.40264221, 0.35275561, 0.39938872, 0.38844528, 0.40796609,\n",
              "       0.3987972 , 0.42797988, 0.44355714, 0.43557136, 0.39386769,\n",
              "       0.38854381, 0.42206448, 0.41368434, 0.3895297 , 0.39445921,\n",
              "       0.40057182, 0.40234645, 0.38765654, 0.40983932, 0.41821947,\n",
              "       0.44681064, 0.44986691, 0.46652867, 0.49078184, 0.51680964,\n",
              "       0.49945783, 0.53090796, 0.50724638, 0.53159817, 0.52824614,\n",
              "       0.5349502 , 0.51789414, 0.52173915, 0.50635911, 0.52982362,\n",
              "       0.53376716, 0.53051367, 0.52252789, 0.52154193, 0.50162682,\n",
              "       0.51227443, 0.46603569, 0.46850052, 0.4846693 , 0.48930305,\n",
              "       0.45617667, 0.44690925, 0.41595187, 0.43764175, 0.46209207,\n",
              "       0.45045843, 0.43645864, 0.43488124, 0.46071181, 0.44217689,\n",
              "       0.45864143, 0.45666965, 0.45617667, 0.44069802, 0.4340925 ,\n",
              "       0.430839  , 0.43852902, 0.42354335, 0.44671203, 0.44681064,\n",
              "       0.45509218, 0.45893719, 0.44888102, 0.46110618, 0.49196488,\n",
              "       0.48368334, 0.48871138, 0.50488017, 0.49176781, 0.50990836,\n",
              "       0.52647144, 0.52371092, 0.52085187, 0.51789414, 0.53435868,\n",
              "       0.54964019, 0.55585141, 0.55890769, 0.58542846, 0.60573802,\n",
              "       0.63087854, 0.61776603, 0.63620234, 0.63018833, 0.64142761,\n",
              "       0.6760327 , 0.66942718, 0.64882187, 0.60997732, 0.64645565,\n",
              "       0.63906139, 0.6528641 , 0.67021592, 0.67445537, 0.65256834,\n",
              "       0.63492062, 0.64142761, 0.6435966 , 0.64024457, 0.65838513,\n",
              "       0.6543429 , 0.65542739, 0.64635712, 0.64586414, 0.64103331,\n",
              "       0.6435966 , 0.67346941, 0.67553987, 0.69545498, 0.68628616,\n",
              "       0.64852611, 0.65187814, 0.66656813, 0.68155388, 0.68066645,\n",
              "       0.66824415, 0.66015968, 0.67061036, 0.71517301, 0.70245495,\n",
              "       0.709159  , 0.72039828, 0.72680673, 0.75036977, 0.73479251,\n",
              "       0.7245392 , 0.67425815, 0.6799764 , 0.66587793, 0.67484967,\n",
              "       0.67248345, 0.64556838, 0.61480829, 0.61963911, 0.64349807,\n",
              "       0.65315986, 0.65404714, 0.63876563, 0.60465352, 0.61372379,\n",
              "       0.60061128, 0.61194909, 0.577344  , 0.59676627, 0.60554079,\n",
              "       0.61825886, 0.61441385, 0.61352657, 0.60070982, 0.5947945 ,\n",
              "       0.62289261, 0.63354036, 0.65039935, 0.67218769, 0.6771172 ,\n",
              "       0.6792862 , 0.67149764, 0.67100466, 0.67770887, 0.67307512,\n",
              "       0.70975067, 0.68244115, 0.67415962, 0.68461015, 0.69910292,\n",
              "       0.69387765, 0.69703246, 0.68875092, 0.69239871, 0.66390615,\n",
              "       0.66341317, 0.68431439, 0.68441293, 0.69427194, 0.71882095,\n",
              "       0.76200334, 0.78842558, 0.79305933, 0.79690434, 0.80212961,\n",
              "       0.75155281, 0.78536931, 0.83525591, 0.83003065, 0.82007294,\n",
              "       0.80114365, 0.83545314, 0.89322683, 0.93167709, 0.92655036,\n",
              "       0.97476094, 0.93818407, 0.92428282, 0.97328215, 0.90387459,\n",
              "       0.89283254, 0.87912851, 0.91107178, 0.93719811, 0.94350788,\n",
              "       0.98343692, 0.97318346, 0.97407089, 0.96243717, 0.95622609,\n",
              "       1.        , 0.9772257 , 0.93009961, 0.9013113 , 0.90298731,\n",
              "       0.90318453, 0.93167709, 0.93611361, 0.90318453, 0.91186052,\n",
              "       0.87962149, 0.84442473, 0.82746721, 0.80676336, 0.79897465,\n",
              "       0.7808341 , 0.77994682, 0.77531307, 0.88484676, 0.92871935,\n",
              "       0.92704334, 0.93916988, 0.91018435, 0.90515631, 0.89795927,\n",
              "       0.92921233, 0.94350788, 0.90249433, 0.86818499, 0.87055121,\n",
              "       0.90909985, 0.90673379, 0.87055121, 0.85497395, 0.82559412,\n",
              "       0.7836933 , 0.81001685, 0.83081939, 0.83348121, 0.81455192,\n",
              "       0.84767822, 0.84442473, 0.81425616, 0.77610181, 0.75776404,\n",
              "       0.81208716, 0.76841179, 0.73104603, 0.69052547, 0.73459529,\n",
              "       0.77896086, 0.78911564, 0.82224194, 0.83604465, 0.8699597 ,\n",
              "       0.88366372, 0.92171953, 0.92812783, 0.93680382, 0.96993012,\n",
              "       0.95819787, 0.92704334, 0.9240856 , 0.96480339, 0.93147986,\n",
              "       0.89963528, 0.90269155, 0.88248054, 0.83969244, 0.8585232 ,\n",
              "       0.88553681, 0.83515723, 0.83298838, 0.85595976, 0.85428374,\n",
              "       0.84629796, 0.80065067, 0.81139711, 0.75145427, 0.74918674,\n",
              "       0.81888991, 0.75983435, 0.76289077, 0.77787636, 0.84235442,\n",
              "       0.75115851, 0.75618656, 0.70472248, 0.72887704, 0.64990637,\n",
              "       0.61106182, 0.65592037, 0.64044165, 0.67692013, 0.59390723,\n",
              "       0.55969642, 0.56206249, 0.61648431, 0.58937201, 0.59094949,\n",
              "       0.62308983, 0.68086367, 0.67297643, 0.67169486, 0.6963424 ,\n",
              "       0.63886432, 0.64635712, 0.67169486, 0.6643006 , 0.61185056,\n",
              "       0.55752743, 0.50576759, 0.51444342, 0.54076698, 0.48782411,\n",
              "       0.50261263, 0.54510497, 0.53997839, 0.5687667 , 0.60218876,\n",
              "       0.60218876, 0.5605837 , 0.57823127, 0.5534852 , 0.57527354,\n",
              "       0.6012028 , 0.61461107, 0.64842758, 0.65523017, 0.63383612,\n",
              "       0.6435966 , 0.63994882, 0.66932864, 0.68608894, 0.65552608,\n",
              "       0.69427194, 0.71438427, 0.73715873, 0.72473627, 0.713497  ,\n",
              "       0.70018741, 0.75135558, 0.75687676, 0.80774917, 0.79789016,\n",
              "       0.78310163, 0.84343892, 0.84028396, 0.83574889, 0.83101646,\n",
              "       0.83150944, 0.87410046, 0.8667062 , 0.90229726, 0.91304355,\n",
              "       0.91146607, 0.92645182, 0.92250812, 0.89657901, 0.85763592,\n",
              "       0.85428374, 0.85724148, 0.88188902, 0.81869269, 0.79660859,\n",
              "       0.7722568 , 0.75559504, 0.76289077, 0.74169379, 0.72907426,\n",
              "       0.74317273, 0.72838421, 0.75707383, 0.81681945, 0.72227152,\n",
              "       0.73676428, 0.70777875, 0.69131421, 0.72858128, 0.75244008,\n",
              "       0.72108848, 0.71142669, 0.68865223, 0.69200441, 0.70176474,\n",
              "       0.68283545, 0.61027308, 0.5680765 , 0.60997732, 0.64596282,\n",
              "       0.64892041, 0.63935715, 0.58671003, 0.58996353, 0.57576652,\n",
              "       0.56945676, 0.61530127, 0.5698512 , 0.60958303, 0.62279407,\n",
              "       0.62387857, 0.61924482, 0.65749785, 0.67899044, 0.70748299,\n",
              "       0.67800463, 0.63314607, 0.74100374, 0.71734201, 0.69082123,\n",
              "       0.6354136 , 0.57478071, 0.5698512 , 0.575175  , 0.58089325,\n",
              "       0.53524595, 0.65355416, 0.68145519, 0.66745541, 0.68480722,\n",
              "       0.67248345, 0.69151143, 0.69713099, 0.66479343, 0.68618748,\n",
              "       0.69496215, 0.66577939, 0.62742782, 0.59735779, 0.66499065,\n",
              "       0.66775116, 0.66282166, 0.65118809, 0.61451253, 0.59509026,\n",
              "       0.61194909, 0.60711827, 0.6300898 , 0.63975159, 0.61747027,\n",
              "       0.5513162 , 0.5316967 , 0.51059841, 0.50990836, 0.5409642 ,\n",
              "       0.50921816, 0.50557037, 0.48752835, 0.44819089, 0.48338758,\n",
              "       0.48654239, 0.43862764, 0.45134578, 0.43813465, 0.48348612,\n",
              "       0.48871138, 0.49442963, 0.52164061, 0.52085187, 0.53416146,\n",
              "       0.54579518, 0.53859813, 0.53918965, 0.56482301, 0.59676627,\n",
              "       0.61076606, 0.60416054, 0.62486453, 0.64428666, 0.61539981,\n",
              "       0.62811787, 0.63935715, 0.69249739, 0.7287785 , 0.70146898,\n",
              "       0.73025729, 0.70334222, 0.69299022, 0.69437048, 0.7223702 ,\n",
              "       0.71596175, 0.7369615 , 0.72098995, 0.70955345, 0.66942718,\n",
              "       0.67366663, 0.67849746, 0.65197683, 0.66390615, 0.65887811,\n",
              "       0.63817411, 0.64408958, 0.6945677 , 0.72217298, 0.70018741,\n",
              "       0.71270826, 0.69022971, 0.6696244 , 0.68904668, 0.70994774,\n",
              "       0.71389144, 0.74208824, 0.73370801, 0.75736959, 0.77590459,\n",
              "       0.76160905, 0.77245387, 0.78546785, 0.76604557, 0.75983435,\n",
              "       0.79059458, 0.80627038, 0.83131222, 0.84383321, 0.83850941,\n",
              "       0.82007294, 0.82894615, 0.80301689, 0.79089034, 0.78398906,\n",
              "       0.8378192 , 0.83436864, 0.83456571, 0.84679095, 0.85822744,\n",
              "       0.8485655 , 0.8324954 ])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "id": "WDIHSvbHN1vk"
      },
      "id": "WDIHSvbHN1vk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMFgAgL5N6HX",
        "outputId": "0f80a24c-a3bb-4ea1-8566-e0ac9f6a7c16"
      },
      "id": "QMFgAgL5N6HX",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.00985902])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scale_factor = 1/0.00985902\n",
        "y_pred = y_pred * scale_factor\n",
        "y_test = y_test * scale_factor"
      ],
      "metadata": {
        "id": "tPcp9BKLN-Y8"
      },
      "id": "tPcp9BKLN-Y8",
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12,6))\n",
        "plt.plot(y_test, 'b', label = \"Original Price\")\n",
        "plt.plot(y_pred, 'r', label = \"Predicted Price\")\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "phumyMZUOPp_",
        "outputId": "97c097f5-425b-4b78-c16e-83c45eb6514b"
      },
      "id": "phumyMZUOPp_",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model evaluation"
      ],
      "metadata": {
        "id": "m54ljCJrpShP"
      },
      "id": "m54ljCJrpShP"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "print(\"Mean absolute error on test set: \", mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx6JWOKcX3Yo",
        "outputId": "60556eef-f2e1-45f6-c9f1-533107e2070a"
      },
      "id": "Yx6JWOKcX3Yo",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error on test set:  4.542532119013522\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}